The global energy landscape is rapidly shifting towards renewable
sources, notably photovoltaic (PV) installations. However, the PV reliance on
meteorological conditions poses challenges to grid stability. Leveraging machine
learning (ML) models offers a promising solution by accurately predicting energy
generation. ML's ability to tackle complex patterns and continuously learn from
historical data enables the development of adaptive forecasting models, enhancing the
efficiency of PV installations and advancing sustainable energy practices.
